

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''image_processor''', '''tokenizer''']
							UpperCAmelCase_						:		List[str]								=	'''BlipImageProcessor'''
							UpperCAmelCase_						:		int								=	'''AutoTokenizer'''




							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[Any]:
														A      :					Tuple      		=					False
														super().__init__(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					List[str]      		=					self.image_processor




							def __call__(					self    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       BatchEncoding:
														if images is None and text is None:
																					raise ValueError('''You have to specify either images or text.'''						)

														# Get only text
														if images is None:
																					A      :					Optional[int]      		=					self.tokenizer
																					A      :					List[Any]      		=					self.tokenizer(
																					    text=__UpperCAmelCase    ,						add_special_tokens=__UpperCAmelCase    ,						padding=__UpperCAmelCase    ,						truncation=__UpperCAmelCase    ,						max_length=__UpperCAmelCase    ,						stride=__UpperCAmelCase    ,						pad_to_multiple_of=__UpperCAmelCase    ,						return_attention_mask=__UpperCAmelCase    ,						return_overflowing_tokens=__UpperCAmelCase    ,						return_special_tokens_mask=__UpperCAmelCase    ,						return_offsets_mapping=__UpperCAmelCase    ,						return_token_type_ids=__UpperCAmelCase    ,						return_length=__UpperCAmelCase    ,						verbose=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
																					return text_encoding

														# add pixel_values
														A      :					Optional[Any]      		=					self.image_processor(__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase						)

														if text is not None:
																					A      :					List[str]      		=					self.tokenizer(
																					    text=__UpperCAmelCase    ,						add_special_tokens=__UpperCAmelCase    ,						padding=__UpperCAmelCase    ,						truncation=__UpperCAmelCase    ,						max_length=__UpperCAmelCase    ,						stride=__UpperCAmelCase    ,						pad_to_multiple_of=__UpperCAmelCase    ,						return_attention_mask=__UpperCAmelCase    ,						return_overflowing_tokens=__UpperCAmelCase    ,						return_special_tokens_mask=__UpperCAmelCase    ,						return_offsets_mapping=__UpperCAmelCase    ,						return_token_type_ids=__UpperCAmelCase    ,						return_length=__UpperCAmelCase    ,						verbose=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														else:
																					A      :					List[str]      		=					None

														if text_encoding is not None:
																					encoding_image_processor.update(__UpperCAmelCase						)

														return encoding_image_processor




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														return self.tokenizer.batch_decode(*__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Union[str, Any]:
														return self.tokenizer.decode(*__UpperCAmelCase    ,						**__UpperCAmelCase						)






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					List[str]      		=					self.tokenizer.model_input_names
														A      :					Optional[int]      		=					self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)









import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






